Methods

We used cross-sectional data from the Johnston County Osteoarthritis Project. The
sample (n = 2092) had a mean age of 65 ± 11 years, body mass index (BMI) 31 ± 7 kg/m2, with 33% men and 34% African Americans. A single expert reader (intra-rater κ =
0.89) provided radiographic grades based on standard atlases for the hands (30 joints,
including bilateral distal and proximal interphalangeal [IP], thumb IP, metacarpophalangeal
[MCP] and carpometacarpal [CMC] joints), knees (patellofemoral and tibiofemoral, 4
joints), hips (2 joints), and spine (5 levels [L1/2 to L5/S1]). All grades were entered
into an exploratory common factor analysis as continuous variables. Stratified factor
analyses were used to look for differences by gender, race, age, and cohort subgroups.

Results

Four factors were identified as follows: IP/CMC factor (20 joints), MCP factor (8
joints), Knee factor (4 joints), Spine factor (5 levels). These factors had high internal
consistency reliability (Cronbach's α range 0.80 to 0.95), were not collapsible into
a single factor, and had moderate between-factor correlations (Pearson correlation
coefficient r = 0.24 to 0.44). There were no major differences in factor structure
when stratified by subgroup.

Conclusions

The 4 factors obtained in this analysis indicate that the variables contained within
each factor share an underlying cause, but the 4 factors are distinct, suggesting
that combining these joint sites into one overall measure is not appropriate. Using
such factors to reflect multi-joint rOA in statistical models can reduce the number
of variables needed and increase precision.